{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gc\n",
    "import os\n",
    "from pathlib import Path\n",
    "import random\n",
    "import sys\n",
    "\n",
    "from tqdm import tqdm_notebook as tqdm\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "from sklearn import preprocessing\n",
    "from sklearn.model_selection import KFold\n",
    "\n",
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas.api.types import is_datetime64_any_dtype as is_datetime\n",
    "from pandas.api.types import is_categorical_dtype\n",
    "\n",
    "def reduce_mem_usage(df, use_float16=False):\n",
    "    \"\"\" iterate through all the columns of a dataframe and modify the data type\n",
    "        to reduce memory usage.        \n",
    "    \"\"\"\n",
    "    start_mem = df.memory_usage().sum() / 1024**2\n",
    "    print('Memory usage of dataframe is {:.2f} MB'.format(start_mem))\n",
    "    \n",
    "    for col in df.columns:\n",
    "        if is_datetime(df[col]) or is_categorical_dtype(df[col]):\n",
    "            # skip datetime type or categorical type\n",
    "            continue\n",
    "        col_type = df[col].dtype\n",
    "        \n",
    "        if col_type != object:\n",
    "            c_min = df[col].min()\n",
    "            c_max = df[col].max()\n",
    "            if str(col_type)[:3] == 'int':\n",
    "                if c_min > np.iinfo(np.int8).min and c_max < np.iinfo(np.int8).max:\n",
    "                    df[col] = df[col].astype(np.int8)\n",
    "                elif c_min > np.iinfo(np.int16).min and c_max < np.iinfo(np.int16).max:\n",
    "                    df[col] = df[col].astype(np.int16)\n",
    "                elif c_min > np.iinfo(np.int32).min and c_max < np.iinfo(np.int32).max:\n",
    "                    df[col] = df[col].astype(np.int32)\n",
    "                elif c_min > np.iinfo(np.int64).min and c_max < np.iinfo(np.int64).max:\n",
    "                    df[col] = df[col].astype(np.int64)  \n",
    "            else:\n",
    "                if use_float16 and c_min > np.finfo(np.float16).min and c_max < np.finfo(np.float16).max:\n",
    "                    df[col] = df[col].astype(np.float16)\n",
    "                elif c_min > np.finfo(np.float32).min and c_max < np.finfo(np.float32).max:\n",
    "                    df[col] = df[col].astype(np.float32)\n",
    "                else:\n",
    "                    df[col] = df[col].astype(np.float64)\n",
    "        else:\n",
    "            df[col] = df[col].astype('category')\n",
    "\n",
    "    end_mem = df.memory_usage().sum() / 1024**2\n",
    "    print('Memory usage after optimization is: {:.2f} MB'.format(end_mem))\n",
    "    print('Decreased by {:.1f}%'.format(100 * (start_mem - end_mem) / start_mem))\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = '../../../../Resources/'\n",
    "test_df = pd.read_csv(path + 'test.csv')\n",
    "building_meta_df = pd.read_csv(path + 'building_metadata.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "leak_df = pd.read_feather('../../../../Large_output/leaking/leak.feather')\n",
    "leak_df.fillna(0, inplace=True)\n",
    "leak_df = leak_df[(leak_df.timestamp.dt.year > 2016) & (leak_df.timestamp.dt.year < 2019)]\n",
    "leak_df.loc[leak_df.meter_reading < 0, 'meter_reading'] = 0 # remove large negative values\n",
    "leak_df = leak_df[leak_df.building_id!=245]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/local/home/ningzesun/.local/lib/python3.6/site-packages/numpy/lib/arraysetops.py:568: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  mask |= (ar1 == a)\n"
     ]
    }
   ],
   "source": [
    "# cat_clean, lgb3_clean, lgb3_clean, no_0, nn_clean, xgb_first\n",
    "sub_path = '../../../../Large_output/good_submission/'\n",
    "sub_list = ['cat_1.074.csv', 'lgb3_1.081.csv', 'lgb_half_1.089.csv', 'lgb3_1.082.csv', 'xgb_new_noM_1.084.csv', \n",
    "            'nn_clean_1.100.csv']\n",
    "sample_submission1 = pd.read_csv(sub_path + sub_list[0], index_col=0)\n",
    "sample_submission2 = pd.read_csv(sub_path + sub_list[1], index_col=0)\n",
    "sample_submission3 = pd.read_csv(sub_path + sub_list[2], index_col=0)\n",
    "sample_submission4 = pd.read_csv(sub_path + sub_list[3], index_col=0)\n",
    "sample_submission5 = pd.read_csv(sub_path + sub_list[4], index_col=0)\n",
    "sample_submission6 = pd.read_csv(sub_path + sub_list[5], index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df['pred1'] = sample_submission1.meter_reading\n",
    "test_df['pred2'] = sample_submission2.meter_reading\n",
    "test_df['pred3'] = sample_submission3.meter_reading\n",
    "test_df['pred4'] = sample_submission4.meter_reading\n",
    "test_df['pred5'] = sample_submission5.meter_reading\n",
    "test_df['pred6'] = sample_submission6.meter_reading\n",
    "\n",
    "del  sample_submission1,  sample_submission2,  sample_submission3,sample_submission4,sample_submission5,sample_submission6\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df[\"timestamp\"] = pd.to_datetime(test_df[\"timestamp\"], format='%Y-%m-%d %H:%M:%S')\n",
    "leak_df[\"timestamp\"] = pd.to_datetime(leak_df[\"timestamp\"], format='%Y-%m-%d %H:%M:%S')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "leak_df = leak_df.merge(test_df[['building_id', 'meter', 'timestamp', 'pred1',\\\n",
    "                                 'pred2', 'pred3','pred4','pred5','pred6',\\\n",
    "                                 'row_id']], on = ['building_id', 'meter', 'timestamp'], how = \"left\")\n",
    "leak_df = leak_df.merge(building_meta_df[['building_id', 'site_id']], on='building_id', how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "leak_df['pred1_l1p'] = np.log1p(leak_df.pred1)\n",
    "leak_df['pred2_l1p'] = np.log1p(leak_df.pred2)\n",
    "leak_df['pred3_l1p'] = np.log1p(leak_df.pred3)\n",
    "leak_df['pred4_l1p'] = np.log1p(leak_df.pred4)\n",
    "leak_df['pred5_l1p'] = np.log1p(leak_df.pred5)\n",
    "leak_df['pred6_l1p'] = np.log1p(leak_df.pred6)\n",
    "leak_df['meter_reading_l1p'] = np.log1p(leak_df.meter_reading)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sko.PSO import PSO\n",
    "from sko.GA import GA\n",
    "# Define the genetic algorithm function\n",
    "def optimization(p):\n",
    "    w1,w2,w3,w4,w5,w6 = p\n",
    "    v = w1 * leak_df['pred1_l1p'].values + w2 * leak_df['pred2_l1p'].values + w3 * leak_df['pred3_l1p'] +\\\n",
    "    w4 * leak_df['pred4_l1p'].values + w5 * leak_df['pred5_l1p'].values + w6 * leak_df['pred6_l1p']\n",
    "    print(np.sqrt(mean_squared_error(v, leak_df.meter_reading_l1p)))\n",
    "    return np.sqrt(mean_squared_error(v, leak_df.meter_reading_l1p))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "0.9532368089715948\n",
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      "0.9531772169727224\n",
      "0.9531941731268011\n",
      "0.9531771219747632\n",
      "0.9531835785798516\n",
      "0.9531775020546333\n",
      "0.953180160583332\n",
      "0.9531790818957433\n",
      "0.9531974560953433\n",
      "0.9531808389029495\n",
      "0.9531793869240877\n",
      "0.9531770884145659\n",
      "0.9533450474605328\n",
      "0.9537384062238847\n",
      "0.9531795773391071\n",
      "best_x is  [0.46433166 0.0261335  0.09324634 0.24939189 0.13170486 0.01828228] best_y is 0.9531770734236297\n"
     ]
    }
   ],
   "source": [
    "#Run this: PSO algorithm iter 100 test to test the score\n",
    "from sko.PSO import PSO\n",
    "pso = PSO(func=optimization, dim=6, pop=200, max_iter=100, lb=[0,0,0,0,0,0], ub=[0.5,0.5,0.5,0.5,0.5,0.5], w=0.8, c1=0.5, c2=0.5)\n",
    "pso.run()\n",
    "print('best_x is ', pso.gbest_x, 'best_y is', pso.gbest_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best_x is  [0.46433166 0.0261335  0.09324634 0.24939189 0.13170486 0.01828228] best_y is 0.9531770734236297\n"
     ]
    }
   ],
   "source": [
    "print('best_x is ', pso.gbest_x, 'best_y is', pso.gbest_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "w1 = 0.46433166\n",
    "w2 = 0.0261335\n",
    "w3 = 0.09324634\n",
    "w4 = 0.24939189\n",
    "w5 = 0.13170486\n",
    "w6 = 0.01828228"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_submission = pd.read_csv(path + 'sample_submission.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df['pred1_l1p'] = np.log1p(test_df.pred1)\n",
    "test_df['pred2_l1p'] = np.log1p(test_df.pred2)\n",
    "test_df['pred3_l1p'] = np.log1p(test_df.pred3)\n",
    "test_df['pred4_l1p'] = np.log1p(test_df.pred4)\n",
    "test_df['pred5_l1p'] = np.log1p(test_df.pred5)\n",
    "test_df['pred6_l1p'] = np.log1p(test_df.pred6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_submission['meter_reading'] = np.expm1(w1 * test_df['pred1_l1p'] +  w2 * test_df['pred2_l1p'] + \\\n",
    "w3 * test_df['pred3_l1p']+w4 * test_df['pred4_l1p']+w5 * test_df['pred5_l1p']+w6 * test_df['pred6_l1p'])\n",
    "sample_submission.loc[sample_submission.meter_reading < 0, 'meter_reading'] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reindex the row_id, put the meter_reading into sample_submission\n",
    "leak_df = leak_df[['meter_reading', 'row_id']].set_index('row_id').dropna()\n",
    "sample_submission.loc[leak_df.index, 'meter_reading'] = leak_df['meter_reading']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save the submission\n",
    "sample_submission.to_csv('../../../../Large_output/ensemble_avg_final.csv', index=False, float_format='%.4f')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "   row_id  meter_reading\n",
       "0       0     159.888014\n",
       "1       1      72.710420\n",
       "2       2       9.396655\n",
       "3       3     255.643778\n",
       "4       4    1148.467358"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_submission.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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